Paper Info
Title
CANS: Communication Limited Camera Network Self-Configuration for Intelligent Industrial Surveillance
Abstract
Realtime and intelligent video surveillance via camera networks involve computation-intensive vision detection tasks with massive video data, which is crucial for safety in the edge-enabled industrial Internet of Things (IIoT). Multiple video streams compete for limited communication resources on the link between edge devices and camera networks, resulting in considerable communication congestion. It postpones the completion time and degrades the accuracy of vision detection tasks. Thus, achieving high accuracy of vision detection tasks under the communication constraints and vision task deadline constraints is challenging. Previous works focus on single camera configuration to balance the tradeoff between accuracy and processing time of detection tasks by setting video quality parameters. In this paper, an adaptive camera network self-configuration method (CANS) of video surveillance is proposed to cope with multiple video streams of heterogeneous quality of service (QoS) demands for edge-enabled IIoT. Moreover, it adapts to video content and network dynamics. Specifically, the tradeoff between two key performance metrics, i.e., accuracy and latency, is formulated as an NP-hard optimization problem with latency constraints. A low-complexity algorithm is proposed to solve the optimization problem based on greedy searching. Simulation on real-world surveillance datasets demonstrates that the proposed CANS method achieves low end-to-end latency (13 ms on average) with high accuracy (92%) with network dynamics, which validates its effectiveness.
Year
DOI
Venue
2021
10.1109/ECON48115.2021.9589140
IECON 2021 - 47TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY
Keywords
DocType
ISSN
Edge computing, video surveillance, object detection, adaptive video configuration
Conference
1553-572X
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Jingzheng Tu101.01
Qimin Xu201.69
Cai-Lian Chen383198.98